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Global VC investments rose 5.4% to $368.5B in 2024, but deals fell 17% | NVCA/Pitchbook

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Global venture capital investments rose to $368.5 billion in 2024, up 5.4% from $349.4 billion a year earlier, according to the first look at the Q4 2024 Pitchbook-NVCA Venture Monitor report. But the number of global […]

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Global venture capital investments rose to $368.5 billion in 2024, up 5.4% from $349.4 billion a year earlier, according to the first look at the Q4 2024 Pitchbook-NVCA Venture Monitor report.

But the number of global deals in 2024 fell 17% to 35,686 from 43,320 a year earlier in 2023. AI deals as a percentage of all deals rose for the year, as you can see in the chart below.

The 2024 global deals are down 50.9% from $751.5 billion in the peak year of 2021 and down 37% from 57,068 in deal count in 2021.

AI deals are big part of the picture now. There were 8,343 global AI deals in 2024, down 3.6% from 8,661 in 2023 and down 16.6% from 10,007 in 2021.

AI’s share of all global VC deals is at a new high.

The value of those global AI deals in 2024 was $131.5 billion, up from 52% from $86.3 billion in 2023 and down 6% from $140.2 billion in 2021.

AI and machine learning were 35.7% of global deal value in 2024, up from 24.7% in 2023. And AI and machine learning were 23.4% of the global deal count in 2024, up from 20% in 2023. In 2021, AI was 18.7% of global deal value and 17.5% of global deal count.

Q4 global numbers

On the global level in Q4, Asia Pacific’s venture market has struggled through the last few years, something that didn’t change in 2024, Pitchbook lead VC analyst Kyle Stanford said.

Compared with Europe and the U.S., the amount of dry powder built up within the various markets across APAC was much smaller, further pressuring dealmaking over the past year. China, which has driven around half of the annual deal activity for APAC, has seen a material decline in activity, due to both economic challenges within the country, as well as the tensions with the U.S. government, which has curtailed activity by U.S.-headquartered firms. Just 20.4% of deal count occurred in Asia, the lowest proportion in the past decade.

Globally, AI has continued to dominate the headlines and investment focus of investors despite some noting that the investment activity is not sustainable long-term. Whether or not that true is trivial in the current moment.

Just over half of all VC invested globally during Q4 went to an AI-focused company. Its true that amount was heavily influenced by the likes of OpenAI, Databricks, xAI, and other well-known companies raising for share buybacks and investment into chips and computing energy needs, but the most important factors is the level of capital availability for AI compared with other sectors, Stanford said.

The proportion of total deals going to AI companies has consistently increased over the past couple years as large corporates and investors alike move to harness the expected efficiencies of the next tech wave, he said.

Global VC investments and deal counts by year.

“VC-backed exits have not been strong historically for APAC, though many markets are still too young to develop a healthy exit environment,” he said. “The lack of exits across many of the regions has kept many foreign investors weary of increased activity during the market slowdown. Japan has been an outlier in terms of count, as many IPOs within the country have helped drive returns to investors. In 2024, 19% of the global VC-backed exits originated in Asia-based companies.”

Fundraising has been slow globally, as new commitments dropped just over 20% YoY. The lack of exits has had a large impact on fundraising for Asia as LPs have been less inclined to reup commitments at this time. 2024 marked the lowest year for new commitments since 2018, and was the lowest year for closed funds in the market in the past decade. North America and Europe similarly struggled to secure new commitments to venture funds.

Q4 U.S. deals

U.S. Dealmaking remained relatively robust in the fourth quarter of 2024 from a count perspective, and increased slightly by 3.7% compared to a year earlier, Pitchbook and the NVCA said. In the quarter, AI deals accounted for nearly half (46.4%) of total US deal value.

Stanford said it seems counterintuitive to the narrative in the market over the past few years, but is indicative of holdover of certain mechanics of venture from a few years ago.

“What has happened is that the excess of dry powder from the high fundraising years of 2021 and 2022 have kept many investors active in the market despite the lack of returns,” Stanford said. “With the slow fundraising years of 2023 and 2024, we should likely see this relative robustness start to deteriorate as fund run through their available capital and aren’t able to raise a subsequent fund.”

AI deals by year has been rising sharply.

Artificial intelligence continues to be the story of the market, and drove a near majority of dollars for VC in 2024, he said. OpenAI, xAI, Anthropic, and others have become synonymous with outsized deals in venture, and seemingly operate in a different funding environment than most VC-backed companies who continue to struggle with lower capital availability, Stanford said.

But the lack of exits remains the story of the venture market, even as the outlook is more hopeful, he said. Just $149.2 billion in exit value was created during 2024, largely coming from a handful of IPOs. Unicorns, which hold around two-thirds of the U.S. VC market value, have held tight as private companies, creating pressure on investors and limited partners with the lack of distributions.

Merges and acquisitions were was also “silent in 2024,” with few large deals to note, Stanford said. A more acquisition-friendly environment in 2025 could set the stage for a renewed M&A market, especially if a soft-landing for the economy can be fully engineered, he said.

In the U.S., fundraising was dominated by large, established firms. Thirty firms accounted for more than 68% of total fundraising value in 2024. This is a trend that has been developing over the past few years, but hit a forefront last year, Stanford said.

Many of the emerging managers that raised funds during the ZIRP-era boom in the VC market have been unable to generate returns, and have portfolios troubled from the valuation changes that have occurred during the market shift. Without a track record to speak to, many firms are finding a very challenging market to raise new commitments from LPs, Stanford said.

European VC market

In Europe, VC deal value reflected a slight decline, while deal counts dropped by roughly 16% compared to year ago, said Pitchbook analyst Nalin Patel, as a more cautious environment was on display in 2024.

European deal activity was down across earlier financing stages, the majority of verticals, and several regions as tougher market for funding was evident.

He said AI drove just over a quarter of deal value to the region during 2024, on just more than 23% of completed financings. The large, outsized deals attributable to other venture markets did not materialize in the same amount in Europe, keeping the proportion of deal value in line with count.

And he said exit value picked up in 2024, largely driven by the listing of Puif. Otherwise it was a quiet year for European VC-backed exits, particularly on the listings front as companies avoided exits.

“We expect exits to pick up in 2025 as market conditions improve,” Patel said.

Capital raised by European-based VC funds was flat YoY in 2024 and remained below the peak set in 2022. Fund counts also dipped in 2024 dropping by approximately by a fifth compared to 2023. Lower fund counts and flat capital raised figures indicate fewer, but larger funds closed in 2024.

The outlook?

One way to look at how much dry powder the industry has and whether VCs are successful themselves is to look how well they have done raising money themselves. That’s where the news looks fairly bleak, or at least is corrected now compared to the overhyped days of 2021.

In 2024, 1,344 funds raised capital, down from 2,333 in 2023 and a record 4,283 in 2021. In terms of capital raised, the 1,344 VCs raised $169.7 billion in 2024, down from $213.8 billion in 2023 and down from the record $404.4 billion in 2021.

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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